Stochastic Environmental Research and Risk Assessment最新文献

{"title":"Spatial pattern of bias in areal rainfall estimations and its impact on hydrological modeling: a comparative analysis of estimating areal rainfall based on radar and weather station networks in South Korea","authors":"Byung-Jin So, Hyung-Suk Kim, Hyun-Han Kwon","doi":"10.1007/s00477-024-02714-2","DOIUrl":"https://doi.org/10.1007/s00477-024-02714-2","url":null,"abstract":"<p>Areal rainfall is routinely estimated based on the observed rainfall data using distributed point rainfall gauges. However, the data collected are sparse and cannot represent the continuous rainfall distribution (or field) over a large watershed due to the limitations of weather station networks. Recent improvements in remote-sensing-based rainfall estimation facilitate more accurate and effective hydrological modeling with a continuous spatial representation of rainfall over a watershed of interest. In this study, we conducted a systematic stepwise comparison of the areal rainfalls estimated by a synoptic weather station and radar station networks throughout South Korea. The bias in the areal rainfalls computed by the automated synoptic observing system and automatic weather system networks was analyzed on an hourly basis for the year 2021. The results showed that the bias increased significantly for hydrological analysis; more importantly, the identified bias exhibited a magnitude comparable to that of the low flow. This discrepancy could potentially mislead the overall rainfall-runoff modeling process. Moreover, the areal rainfall estimated by the radar-based approach significantly differed from that estimated by the existing Thiessen Weighting approach by 4%–100%, indicating that areal rainfalls from a limited number of weather stations are problematic for hydrologic studies. Our case study demonstrated that the gauging station density must be within 10 km² on average for accurate areal rainfall estimation. This study recommends the use of radar rainfall networks to reduce uncertainties in the measurement and prediction of areal rainfalls with a limited number of ground weather station networks.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"6 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140584502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Some combinatorics of data leakage induced by clusters","authors":"Fabian Guignard, David Ginsbourger, Lilia Levy Häner, Juan Manuel Herrera","doi":"10.1007/s00477-024-02715-1","DOIUrl":"https://doi.org/10.1007/s00477-024-02715-1","url":null,"abstract":"<p>Data leakage is a common issue that can lead to misleading generalisation error estimation and incorrect hyperparameter tuning. However, its mechanisms are not always well understood. In this work, we consider the case of clustered data and investigate the distribution of the number of elements in leakage when the data set is uniformly split. For both the validation and test sets, the first and second moments of the number of elements in leakage are derived analytically. Modelling consequences are investigated and exemplified on simulated data. In addition, the case of an actual agronomic feasibility study is presented. We demonstrate how data leakage can distort model performance estimation when an inadequate data splitting strategy is used. We provide an understanding of data leakage in the context of clustered data by quantifying its role in predictive modelling. This sheds light on related challenges that may impact the practice in agronomy and beyond.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"120 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140584483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Refinement analysis of landslide risk assessment for wide area based on UAV-acquired high spatial resolution images","authors":"Zhengjun Mao, Haiyong Yu, Xu Ma, Wei Liang, Guangsheng Gao, Yanshan Tian, Shuojie Shi","doi":"10.1007/s00477-024-02688-1","DOIUrl":"https://doi.org/10.1007/s00477-024-02688-1","url":null,"abstract":"<p>The Loess Plateau is the largest loess accumulation zone globally. It has a fragile geological and ecological environment, experiences significant water and soil loss, and is prone to frequent landslides and collapses. Thus, landslide risk assessment and disaster prevention and reduction are required in this region. Using images acquired from unmanned aerial vehicles (UAVs) has the advantages of low cost, flexible data collection, high spatial image resolution, and real-time image data over traditional landslide risk assessment methods. UAV remote sensing has been used to identify and extract single or small loess landslides and determine elements at risk. An effective method is required to conduct wide-area landslide research for land-use planning. We used high spatial resolution (0.13 m) UAV images and Geographic Information Systems (GIS) analysis to update landslide catalog data and extract land use, roads, rivers, and other elements at risk. The frequency ratio coupled with the random forest model was used to evaluate landslide susceptibility; the prediction accuracy was high. The area under the curve (AUC) was 0.791. The risk index was calculated for five rainfall intensities, and the vulnerability evaluation and value estimation of the element at risk were completed by grey correlation model. Susceptibility, hazard, and the loess landslide vulnerability evaluation and value estimation of the elements at risk are combined to realize the fine evaluation of the whole process of the wide-area (164 km²). This study demonstrates that combining high spatial resolution UAV images and GIS is suitable for wide-area loess landslide risk assessment. This approach can be used for wide-area refined risk assessment of loess landslides in areas with similar geological conditions.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"29 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140584479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urbanization vs. climate drivers: investigating changes in fluvial floods in Poland","authors":"Nelson Venegas-Cordero, Luis Mediero, Mikołaj Piniewski","doi":"10.1007/s00477-024-02717-z","DOIUrl":"https://doi.org/10.1007/s00477-024-02717-z","url":null,"abstract":"<p>Fluvial floods are a severe hazard resulting from the interplay of climatic and anthropogenic factors. The most critical anthropogenic factor is urbanization, which increases land imperviousness. This study uses the paired catchment approach to investigate the effect of urbanization vs. climate drivers on river floods in Poland. Long-term daily river flow data until 2020 was used for four selected urban catchments and their non-urban counterparts, along with extreme precipitation, soil moisture excess, and snowmelt data generated from the process-based Soil &amp; Water Assessment Tool (SWAT) model. Changes in impervious areas were assessed using two state-of-the-art Copernicus products, revealing a consistent upward trend in imperviousness across all selected urban catchments. A range of statistical methods were employed to assess changes in the magnitude and frequency of floods and flood drivers, including the Pettitt test, the Mann Kendall (MK) multitemporal test, the Poisson regression test, multi-temporal correlation analysis and multiple linear regression. The MK test results showed a contrasting behaviour between urban (increases) and non-urban (no change) catchments for three of the four analysed catchment pairs. Flood frequency increased significantly in only one urban catchment. Multiple regression analysis revealed that non-urban catchments consistently exhibited stronger relationships between floods and climate drivers than the urban ones, although the results of residual analysis were not statistically significant. In summary, the evidence for the impact of urbanization on floods was found to be moderate. The study highlights the significance of evaluating both climatic and anthropogenic factors when analysing river flood dynamics in Poland.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"42 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140584380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semi-supervised deep learning based on label propagation algorithm for debris flow susceptibility assessment in few-label scenarios","authors":"Qingyu Wang, Changming Wang, Haozhe Tang, Di Wu, Fei Wang","doi":"10.1007/s00477-024-02719-x","DOIUrl":"https://doi.org/10.1007/s00477-024-02719-x","url":null,"abstract":"<p>Regional debris flow susceptibility assessment is an effective method to prevent debris flow hazards, and deep learning is emerging as a novel approach in this discipline with the development of computers. However, when debris flow samples are insufficient, there will be problems like overfitting or misclassification. To overcome these problems, this paper proposes a semi-supervised deep neural network model (LPA-DNN) combined with label propagation algorithm (LPA), which utilizes high confidence unlabeled samples as pseudo-samples reasonably in few-label scenarios. Xinzhou, Shanxi Province, was selected as the study area, and a dataset containing 292 debris flow samples and 10 types of impact factors was compiled based on watershed units. Using the dataset and pseudo-samples, the LPA-DNN model was built to get debris flow susceptibility map. Meanwhile, DNN and SVM were set up for comparison to demonstrate that the proposed LPA-DNN model has excellent performance and higher accuracy. LPA-DNN alleviates the problem of low accuracy that caused by samples lacking in deep learning to a certain extent, and obtains great classification results, which proves that it is quite potential in regional debris flow susceptibility assessment.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"18 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140584536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time–frequency characterization of seasonal temperature in India and teleconnection of temperature with atmospheric oscillation indices","authors":"Hareesh Kumar, Nitin Joshi, Himanshu Sharma, Divya Gupta, Shakti Suryavanshi","doi":"10.1007/s00477-024-02703-5","DOIUrl":"https://doi.org/10.1007/s00477-024-02703-5","url":null,"abstract":"<p>The present study focuses on characterizing the time–frequency aspects of seasonal temperatures in India by integrating the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm with the Hilbert–Huang transform (HHT) decomposition method. The investigation also explores the connections between maximum temperature (T_max) and minimum temperature (T_min) with global climate oscillations, such as the El Nino Southern Oscillation (ENSO), Sunspot Number (SN), and Pacific Decadal Oscillations (PDO). The findings indicate that intra and inter-decadal modes play a pivotal role in influencing temperature series across various seasons, with notable changes observed in the amplitudes of inter-decadal modes for seasonal T_min and T_max. The analysis of intrinsic mode functions (IMFs) reveals that IMF2 closely align to ENSO with a periodicity of 5–7 years, IMF3 to the sunspot cycle with a frequency of approximately 11 years, and IMF5 to PDO with a long periodicity exceeding 60 years. The association between the IMF components of T_min and T_max temperature series and the three climate indices is most evident for low-frequency modes, demonstrating a consistent evolution of trend components.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"52 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140584501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the probabilistic drought prediction with soil moisture information under the ensemble streamflow prediction framework","authors":"Gi Joo Kim, Dae Ho Kim, Young-Oh Kim","doi":"10.1007/s00477-024-02710-6","DOIUrl":"https://doi.org/10.1007/s00477-024-02710-6","url":null,"abstract":"<p>Reliable drought prediction should be preceded to prevent damage from potential droughts. In this context, this study developed a hydrological drought prediction method, namely ensemble drought prediction (EDP) to reflect drought-related information under the ensemble streamflow prediction framework. After generating an ensemble of standardized runoff index by converting the ensemble of generated streamflow, the results were adopted as the prior distribution. Then, precipitation forecast and soil moisture were used to update the prior EDP. The EDP + A model included the precipitation forecast with the PDF-ratio method, and the observed soil moisture index was reflected in the former EDP and EDP + A via Bayes’ theorem, resulting in the EDP + S and EDP + AS models. Eight basins in Korea with more than 30 years of observation data were applied with the proposed methodology. As a result, the overall performance of the four EDP models yielded improved results than the climatological prediction. Moreover, reflecting soil moisture yielded improved evaluation metrics during short-term drought predictions, and in basins with larger drainage areas. Finally, the methodology presented in this study was more effective during periods with less intertemporal variabilities.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"30 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140584542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of meteorological, hydrological and agricultural droughts for developing a composite drought index over semi-arid Banas River Basin of India","authors":"Divya Saini, Omvir Singh","doi":"10.1007/s00477-024-02704-4","DOIUrl":"https://doi.org/10.1007/s00477-024-02704-4","url":null,"abstract":"<p>This study attempts to develop a composite index by integrating meteorological, hydrological and agricultural droughts over semi-arid Banas River basin, Rajasthan, India. To affect this, the standardized precipitation index (SPI), streamflow drought index (SDI), and vegetation condition index (VCI) have been used at 1-, 3-, 5-, 9- and 12-month time scales using station and remote sensing-based data for the period 2000 to 2020. To identify the occurrence of above-stated droughts and most vulnerable drought period at different time scales (1-, 3-, 5-, 9- and 12-month) regarding SPI, SDI and VCI has been validated with foodgrains produced and occurrence of historical drought years. This validation has been found significant with SPI-3 (<i>r</i> = − 0.81), SDI-3 (<i>r</i> = − 0.78) and VCI-5 (<i>r</i> = − 0.80) time scales. Subsequently, these time scales have been coalesced using weights obtained from principal component analysis (PCA) to develop the composite drought index (CDI). The annual CDI developed this way has been further validated with foodgrains produced and occurrence of historical drought years. The results of CDI demonstrate the maximum area under mild drought (73 percent) followed by moderate (21 percent) and severe (4 percent), whereas minuscule area has been detected under wet conditions (2 percent). Finally, this study suggests that individual drought types (meteorological, hydrological, agricultural) do not appropriately arrest the drought severity, hence, the usage of multiple droughts based composite index can be more realistic for effective drought assessment and monitoring in hydrologic systems.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"43 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140584375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting the amount of domestic waste clearance in Shenzhen with an optimized grey model","authors":"Bo Zeng, Chao Xia, Yingjie Yang","doi":"10.1007/s00477-024-02706-2","DOIUrl":"https://doi.org/10.1007/s00477-024-02706-2","url":null,"abstract":"<p>As a leading economic center in China and an international metropolis, Shenzhen has great significance in promoting sustainable urban development. To predict its amount of domestic waste clearance, a new multivariable grey prediction model with combinatorial optimization of parameters is established in this paper. Firstly, the new model expands the value range of the order <i>r</i> of a grey accumulation generation operator from positive real numbers (R +) to all real numbers (R), which enlarges the optimization space of parameter and has positive significance for improving model performance. Secondly, the dynamic background-value coefficient <i>λ</i> is introduced into the new model to improve the smoothing effect of the nearest neighbor generated sequences. Thirdly, with the objective function of minimizing the mean absolute percentage error (MAPE), the particle swarm optimization (PSO) is employed to optimize parameters <i>r</i> and <i>λ</i> to improve the overall performance of the new model. The new model is used to simulate and predict the amount of domestic waste clearance in Shenzhen, and the MAPE of the new model is only 0.27%, which is far superior to several other similar models. Lastly, the new model is applied to predict the amount of domestic waste clearance in Shenzhen. The results indicate the amount of domestic waste clearance in 2028 could be 9.96 million tons, an increase of 20.58% compared to 2021.This highlights the significant challenge that Shenzhen faces in terms of urban domestic waste treatment. Therefore, some targeted countermeasures and suggestions have been proposed to ensure the sustainable development of Shenzhen's economy and society.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"120 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140584379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of joint distribution models for tropical cyclone atmospheric parameters in probabilistic coastal hazard analysis","authors":"Ziyue Liu, Meredith L. Carr, Norberto C. Nadal-Caraballo, Luke A. Aucoin, Madison C. Yawn, Michelle T. Bensi","doi":"10.1007/s00477-023-02652-5","DOIUrl":"https://doi.org/10.1007/s00477-023-02652-5","url":null,"abstract":"<p>In probabilistic coastal hazard assessments based on the Joint Probability Method, historical storm data is used to build distribution models of tropical cyclone atmospheric parameters (i.e., central pressure deficit, forward velocity, radius of maximum wind, and heading direction). Recent models have used a range of assumptions regarding the dependence structure between these random variables. This research investigates the performance of a series of joint distribution models based on assumptions of parameter independence, partial-dependence (i.e., dependence between only central pressure deficit and radius of maximum wind), and full dependence (i.e., dependence between each pair of tropical cyclone atmospheric parameters). Full dependence models consider a range of copula models, such as the Gaussian copula and vine copulas that combine linear-circular copulas with Gaussian or Frank copulas. The consideration of linear-circular copulas allows for the characterization of correlation between linear variables (e.g., central pressure deficit) and circular variables (e.g., heading direction). The sensitivity of the results to different joint distribution models is assessed by comparing hazard curves at representative locations in New Orleans, LA (USA). The stability of hazard curves generated using a Gaussian copula considering variation in the selection of the zero-degree convention is also assessed. The tail dependence between large central pressure deficit and large radius of maximum wind associated with various copula models are also compared using estimated conditional probability. It is found that the linear-circular Frank vine copula model improve the stability of hazard curves and maximize tail dependence between large central pressure deficit and large radius of maximum wind. However, the meta-Gaussian copula model exhibits performance within this study region that was generally consistent with the tested vine copulas and have the advantage of being easier to implement.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"120 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140584533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}